#15 Mischief (15-6)

avg: 1830.88  •  sd: 76.88  •  top 16/20: 71.3%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
4 BFG Loss 12-13 1834.6 Jul 8th TCT Pro Elite Challenge West 2023
35 Impact Win 14-13 1642.09 Jul 8th TCT Pro Elite Challenge West 2023
17 Lawless Win 15-12 2063.68 Jul 8th TCT Pro Elite Challenge West 2023
18 Polar Bears Win 13-12 1886.21 Jul 9th TCT Pro Elite Challenge West 2023
4 BFG Loss 7-15 1359.6 Jul 9th TCT Pro Elite Challenge West 2023
28 Flight Club Win 15-8 2200.08 Jul 9th TCT Pro Elite Challenge West 2023
9 Space Force Loss 7-13 1333.17 Aug 19th TCT Elite Select Challenge 2023
21 Love Tractor Win 12-11 1832.03 Aug 19th TCT Elite Select Challenge 2023
35 Impact Win 11-10 1642.09 Aug 19th TCT Elite Select Challenge 2023
9 Space Force Loss 8-10 1628.04 Aug 20th TCT Elite Select Challenge 2023
6 Sprocket Loss 10-15 1471.36 Aug 20th TCT Elite Select Challenge 2023
5 Cleveland Crocs Win 15-10 2390.12 Aug 20th TCT Elite Select Challenge 2023
70 American Barbecue** Win 13-5 1814.93 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
75 Cutthroat Win 13-6 1786.95 Sep 9th 2023 Mixed Nor Cal Sectional Championship
224 Moonlight Ultimate** Win 13-2 927.66 Ignored Sep 9th 2023 Mixed Nor Cal Sectional Championship
33 Tower Win 15-10 2001.98 Sep 9th 2023 Mixed Nor Cal Sectional Championship
26 Sunshine Loss 11-14 1334.08 Sep 10th 2023 Mixed Nor Cal Sectional Championship
18 Polar Bears Win 13-11 1990.05 Sep 23rd 2023 Southwest Mixed Regional Championship
51 Classy Win 14-7 1966.69 Sep 23rd 2023 Southwest Mixed Regional Championship
39 Lotus Win 14-10 1893.74 Sep 23rd 2023 Southwest Mixed Regional Championship
17 Lawless Win 15-8 2327.99 Sep 24th 2023 Southwest Mixed Regional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
What's the algorithm again?
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
Is there a longer explanation of all this?
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)